With the exponential surge in diverse multimodal data, traditional unimodal retrieval
methods struggle to meet the needs of users seeking access to data across various …

Knowledge Graphs (KGs) play a pivotal role in advancing various AI applications, with the
semantic web community's exploration into multi-modal dimensions unlocking new avenues …

Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the
field of natural language processing and artificial intelligence, due to their emergent ability …

This paper presents a detailed study of improving vision features and develops an improved
object detection model for vision language (VL) tasks. Compared to the most widely used …

Image-text matching, as a fundamental task, bridges the gap between vision and language.
The key of this task is to accurately measure similarity between these two modalities. Prior …

Large-scale pre-training methods of learning cross-modal representations on image-text
pairs are becoming popular for vision-language tasks. While existing methods simply …

Image-text matching plays a critical role in bridging the vision and language, and great
progress has been made by exploiting the global alignment between image and sentence …

Recent years have witnessed the resurgence of knowledge engineering which is featured
by the fast growth of knowledge graphs. However, most of existing knowledge graphs are …

Image-text retrieval is a fundamental and vital task in multi-media retrieval and has received
growing attention since it connects heterogeneous data. Previous methods that perform well …

We propose Unicoder-VL, a universal encoder that aims to learn joint representations of
vision and language in a pre-training manner. Borrow ideas from cross-lingual pre-trained …